Set up Julia and write correct, performant, modern Julia for theoretical research — one first choice per task, no ambiguous alternatives. Use whenever the user runs Julia or does numerical experiments, AD/gradients, optimization, polynomial/symbolic computation, or differential equations. Trigger on DifferentiationInterface, ADTypes, ForwardDiff, Enzyme, Zygote, Reactant, JET, DispatchDoctor, AllocCheck, Aqua, ExplicitImports, Chairmarks, OhMyThreads, ComponentArrays, StaticArrays, HomotopyContinuation, SymEngine, Symbolics, ModelingToolkit, SymPyPythonCall, Manopt, DrWatson, Pluto, Documenter, Quarto, Julia setup, experiment/project management, notebooks / literate reports, TTFX / precompile latency, or large-package architecture (module/file organization, include order, submodules vs subpackages, package extensions / weakdeps, type piracy, public API). MANDATORY — read this skill BEFORE writing ANY Julia code. §2.0 forbids FD derivative estimation (use AD), grid sampling (use optimization), and lerp-as-evaluation. All differentiation goes through DifferentiationInterface (raw backend calls are the exception); Dual-propagation rules apply whenever AutoForwardDiff is in the path. Symbolic tooling is chosen by ROLE: SymEngine for lightweight algebra, Symbolics+ModelingToolkit as the spine of AI4S/SciML projects (codegen/PDE/DAE), SymPyPythonCall as a thin-boundary service for heavy CAS (integrate/trigsimp/factor/assumptions).
Install with
Claude CodeCodexCursorGitHub CopilotGemini CLI
Supported by the Atlas install prompt. Skill-specific requirements vary.
Skill stats
Skills1CategoryScience & researchStars0
About this skill
Set up Julia and write correct, performant, modern Julia for theoretical research — one first choice per task, no ambiguous alternatives. Use whenever the user runs Julia or does numerical experiments, AD/gradients, optimization, polynomial/symbolic computation, or differential equations. Trigger on DifferentiationInterface, ADTypes, ForwardDiff, Enzyme, Zygote, Reactant, JET, DispatchDoctor, AllocCheck, Aqua, ExplicitImports, Chairmarks, OhMyThreads, ComponentArrays, StaticArrays, HomotopyContinuation, SymEngine, Symbolics, ModelingToolkit, SymPyPythonCall, Manopt, DrWatson, Pluto, Documenter, Quarto, Julia setup, experiment/project management, notebooks / literate reports, TTFX / precompile latency, or large-package architecture (module/file organization, include order, submodules vs subpackages, package extensions / weakdeps, type piracy, public API). MANDATORY — read this skill BEFORE writing ANY Julia code. §2.0 forbids FD derivative estimation (use AD), grid sampling (use optimization), and lerp-as-evaluation. All differentiation goes through DifferentiationInterface (raw backend calls are the exception); Dual-propagation rules apply whenever AutoForwardDiff is in the path. Symbolic tooling is chosen by ROLE: SymEngine for lightweight algebra, Symbolics+ModelingToolkit as the spine of AI4S/SciML projects (codegen/PDE/DAE), SymPyPythonCall as a thin-boundary service for heavy CAS (integrate/trigsimp/factor/assumptions).
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Set up Julia and write correct, performant, modern Julia for theoretical research — one first choice per task, no ambiguous alternatives. Use whenever the user runs Julia or does numerical experiments, AD/gradients, optimization, polynomial/symbolic computation, or differential equations. Trigger on DifferentiationInterface, ADTypes, ForwardDiff, Enzyme, Zygote, Reactant, JET, DispatchDoctor, AllocCheck, Aqua, ExplicitImports, Chairmarks, OhMyThreads, ComponentArrays, StaticArrays, HomotopyContinuation, SymEngine, Symbolics, ModelingToolkit, SymPyPythonCall, Manopt, DrWatson, Pluto, Documenter, Quarto, Julia setup, experiment/project management, notebooks / literate reports, TTFX / precompile latency, or large-package architecture (module/file organization, include order, submodules vs subpackages, package extensions / weakdeps, type piracy, public API). MANDATORY — read this skill BEFORE writing ANY Julia code. §2.0 forbids FD derivative estimation (use AD), grid sampling (use optimization), and lerp-as-evaluation. All differentiation goes through DifferentiationInterface (raw backend calls are the exception); Dual-propagation rules apply whenever AutoForwardDiff is in the path. Symbolic tooling is chosen by ROLE: SymEngine for lightweight algebra, Symbolics+ModelingToolkit as the spine of AI4S/SciML projects (codegen/PDE/DAE), SymPyPythonCall as a thin-boundary service for heavy CAS (integrate/trigsimp/factor/assumptions).
About this listing
Category
Science & research
Reported license
Not specified
Listing language (estimated)
English
Review status
Not individually reviewed
Agent compatibility
Atlas installation supports Claude Code, Codex, and Cursor. Skill-specific dependencies and features are checked during setup.
Claude Code
Codex
Cursor
GitHub Copilot
Gemini CLI